Methods and apparatus for partitioning wireless network cells into time-based clusters

ABSTRACT

In some embodiments, an apparatus includes a cluster partitioning module and an optimization module. The cluster partitioning module receives a first performance indicator set for a first instance of a time period set. The cluster partitioning module defines a recurring schedule set, where each time period from the recurring schedule set is associated with a performance indicator from the first performance indicator set and within a predefined range of a performance indicator associated with the remaining time periods from the recurring schedule set. The optimization module receives a second performance indicator set for a second instance of the time period set. The optimization module defines a metric value based on the second performance indicator set, and causes a change in a network implementation based on the metric value at each time period from a third instance of the time period set and from the recurring schedule set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 61/557,147, filed Nov. 8, 2011, and entitled“Pattern Recognition and Learning Based Self Optimizing Networks,” whichis incorporated herein by reference in its entirety.

BACKGROUND

Some embodiments described herein relate generally to Self OptimizingNetworks (SON), and, in particular, to methods and apparatus forpartitioning wireless SON cells into time-based clusters.

Some known wireless networks implement an automated system for networkoptimization using SON solutions (e.g., SON products and processes),which adjust radio resources and network parameters to improve theoverall network performance. Such SON solutions typically targetdifferent response times for various network performance indicators.Cells of such wireless networks, however, are typically optimizedindividually without being clustered based on characteristics (e.g.,time-based traffic pattern) of the network performance indicators. Thus,the SON solutions used for optimization of the known wireless networksare typically not customized specifically for such clusters.

Accordingly, a need exists for methods and apparatus for partitioningwireless network cells into clusters and implementing SON solutionscustomized for such clusters, to further improve or maximize the overallnetwork performance for the wireless networks.

In some embodiments, an apparatus includes a cluster partitioning moduleand an optimization module. The cluster partitioning module receives afirst performance indicator set for a first instance of a time periodset. The cluster partitioning module defines a recurring schedule set,where each time period from the recurring schedule set is associatedwith a performance indicator from the first performance indicator setand within a predefined range of a performance indicator associated withthe remaining time periods from the recurring schedule set. Theoptimization module receives a second performance indicator set for asecond instance of the time period set. The optimization module definesa metric value based on the second performance indicator set, and causesa change in a network implementation based on the metric value at eachtime period from a third instance of the time period set and from therecurring schedule set.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram that illustrates a wireless networkconfigured to implement a network optimization device, according to anembodiment.

FIG. 2 is a block diagram of a processor of a network optimizationdevice, according to an embodiment.

FIG. 3 is a schematic illustration of a timeline for operations of anetwork optimization device, according to an embodiment.

FIG. 4 is a flow chart illustrating a method for defining recurringschedule sets and using the recurring schedule sets for optimization,according to an embodiment.

DETAILED DESCRIPTION

In some embodiments, a system includes a cluster partitioning module andan optimization module. The cluster partitioning module or theoptimization module is implemented in at least a memory or a processingdevice. At a first time, the cluster partitioning module is configuredto receive a first set of performance indicators of at least one cell ofa network for a first instance of a set of time periods. The clusterpartitioning module is then configured to define a recurring scheduleset including at least two time periods from the set of time periods.Each time period from the recurring schedule set is associated with atleast one performance indicator from the first set of performanceindicators, which is within a predefined range of at least oneperformance indicator from the first set of performance indicators thatis associated with the remaining time periods from the recurringschedule set.

At a second time after the first time, the optimization module isconfigured to receive a second set of performance indicators of the atleast one cell for a second instance of the set of time periods. Theoptimization module is then configured to define a metric valueassociated with the recurring schedule set based on each performanceindicator from the second set of performance indicators that isassociated with a time period from the recurring schedule set. Theoptimization module is further configured to send the metric value to atleast one network element associated with the at least one cell. As aresult, the network element changes an implementation of the networkbased on the metric value at each time period from a third instance ofthe set of time periods that is included in the recurring schedule set,when the metric value is associated with a network configuration change.

In some instances, the optimization module is, for example, anantenna-based SON process module, a parameter-based SON process module,or a combined antenna- and parameter-based SON process module. In someinstances, the optimization module can be one of, for example, a loadbalancing SON process module, a co-channel interference SON processmodule, a neighbor list SON process module, a handover optimization SONprocess module, or a self-healing SON process module. The SON processesexecuted at the optimization module can be similar to the SON processesshown and described in U.S. Patent Application Publication No.2011/0090820, filed Oct. 16, 2009 and entitled “Self-Optimizing WirelessNetwork,” which is incorporated herein by reference in its entirety.Additionally, in some instances, the metric value is associated with,for example, a tilt of an antenna of a network element.

In some embodiments, a non-transitory processor-readable medium storescode representing instructions to be executed by a processor. The codestored in the medium includes code to cause the processor to receive, ata first time, a first set of performance indicators of at least one cellfor a first instance of a set of time periods. The code stored in themedium includes code to cause the processor to define a first recurringschedule set including at least two time periods from the set of timeperiods. Each time period from the recurring schedule set is associatedwith at least one performance indicator from the first set ofperformance indicators that is within a predefined range of at least oneperformance indicator from the first set of performance indicators andassociated with the remaining time periods from the first recurringschedule set. The code stored in the medium also includes code to causethe processor to send an indication of the first recurring schedule setto a SON process module at a second time after the first time. As aresult, the SON process module performs a SON process for the at leastone cell during each time period from a second instance of the set oftime periods that is included in the first recurring schedule set.Moreover, the code stored in the medium includes code to cause theprocessor to receive, at a third time after the second time, a secondset of performance indicators of the at least one cell for the secondinstance of the set of time periods. The code stored in the mediumfurther includes code to cause the processor to modify the firstrecurring schedule set based on the second set of performance indicatorsto define a second recurring schedule set.

As used herein, a module can be, for example, any assembly and/or set ofoperatively-coupled electrical components, and can include, for example,a memory, a processor, electrical traces, optical connectors, software(executing in hardware) and/or the like. As used herein, the singularforms “a,” “an” and “the” include plural referents unless the contextclearly dictates otherwise. Thus, for example, the term “a networkoptimization device” is intended to mean a single network optimizationdevice or a combination of network optimization devices.

FIG. 1 is a schematic diagram that illustrates a wireless network 100configured to implement a network optimization device 101, according toan embodiment. The wireless network 100 can be similar to the wirelessnetworks shown and described in U.S. Patent Application Publication No.2009/0323530, filed Apr. 17, 2009 and entitled “Dynamic Load Balancing,”and U.S. Patent Application Publication No. 2011/0090820, filed Oct. 16,2009 and entitled “Self-Optimizing Wireless Network,” each of which isincorporated herein by reference in its entirety.

Specifically, the wireless network 100 can be any network that enableswireless communication devices (e.g., cellular phones, Wi-Fi enabledlaptops, Bluetooth devices, mobile devices, etc.) to communicate witheach other. In some embodiments, the wireless network 100 can beimplemented and administered using a wireless transmission system suchas radio frequency (RF) waves. For example, the wireless network 100 canbe a cellular network that enables two cellular phones to communicatewith each other. For another example, the wireless network 100 can be aWi-Fi network that enables multiple Wi-Fi enabled laptops to beoperatively connected. In some embodiments, the wireless network 100 canbe at least a portion of, for example, a wireless local area network(WLAN), a wireless mesh network, a wireless metropolitan area network(MAN), a wireless wide area network (WAN), a mobile device network(e.g., a global system for mobile communications (GSM) network, apersonal communications service (PCS) network), a radio access network(RAN), a long term evolution (LTE) network, a Universal MobileTelecommunications System (UMTS) network, and/or the like.

As shown in FIG. 1, the wireless network 100 includes a networkoptimization device 101 operatively coupled to a network database 120.The network optimization device 101 is also operatively coupled to andconfigured to manage one or more controllers (e.g., controllers 112, 114and 116). Each controller (e.g., the controller 114) is operativelycoupled to and configured to manage one or more network elements such asbase stations (e.g., base stations 142, 144 and 146). A controller canbe any device that is capable of sending control signals (e.g.,commands, instructions, requests, etc.) to the network element(s)controlled by that controller, thus managing operations of the networkelement(s). In some embodiments, a controller can be, for example, aserver or similar computer device. In some embodiments, a controller canalso be considered as a network element of the wireless network 100.

A network element (e.g., base station 142, 144, 146) controlled by acontroller can be any device or infrastructure that can be wirelesslycoupled to and communicate with one or more wireless communicationdevices (e.g., subscribers to the wireless network 100). In someembodiments, such a network element (e.g., base station 146) can beequipped with and configured to control one or more antennas (e.g., theantennas 152 and 154), which can be used to support data communications(e.g., transmit data to and/or receive data from) between the networkelement and the wireless communication devices that are distributedthroughout a coverage area (i.e., sector) associated with that antenna.For example, as shown in FIG. 1, the antenna 152 is used to support datacommunications between the base station 146 and wireless communicationdevices distributed within the coverage area 162; the antenna 154 isused to support data communications between a base station where theantenna 154 is located and wireless communication devices distributedwithin the coverage area 164. In some embodiments, a network element(e.g., base station) controlled by a controller can be located at, forexample, a cell site. In such embodiments, the coverage area associatedwith an antenna of that network element can be referred to as a cell.

In some embodiments, the connections between the network optimizationdevice 101 and the one or more controllers (e.g., the controllers 112,114 and 116) and the network database 120 can include, for example, awireless connection, a wired connection, and/or a combination ofwireless and wired connections. Similarly, the connections between eachcontroller (e.g., the controller 114) and its associated networkelement(s) (e.g., the base stations 142, 144 and 146) can include, forexample, a wireless connection, a wired connection and/or a combinationof wireless and wired connections.

The network database 120 can be implemented in a memory or other storagedevice that is part of the network optimization device 101 or anotherdevice operatively coupled to the network optimization device 101. Thenetwork database 120 can be configured to receive and store informationand/or data associated with the wireless network 100, such as networkstatistics, current network configurations, and performance indicatorsof the wireless network 100. Furthermore, the network database 120 canbe configured to provide the stored information and/or data to thenetwork optimization device 101. The information and/or data can be usedat the network optimization device 101 to assist in partitioning cellsof the wireless network 100 into clusters and improving/optimizing SONprocesses operated in the wireless network 100 based on the clusters, asdescribed in details with respect to FIGS. 2-4. While shown in FIG. 1 asbeing located on a single device, in some embodiments, the functionalityof the network database 120 can be distributed to multiple devices(e.g., multiple databases) across the wireless network 100.

The information and/or data provided from the network database 120 tothe network optimization device 101 can include, for example, a set ofvalues and/or indicators that can be used to determine the performanceof one or more cells of the wireless network 100 in various aspects. Theset of values and/or indicators can include, for example, networkcapacity statistics (e.g., total number of calls, an amount of delivereddata in bits, a throughput measured in overall data rate), KeyPerformance Indicators (KPIs), mobile level measurements, cell levelmeasurements, system level measurements, network metric values,configuration metrics (e.g., system configuration metrics, deviceconfiguration metrics), power indicators, indications of a networkalarm, and/or the like.

In some embodiments, the KPIs provided from the network database 120 tothe network optimization device 101 can include, for example, a neighborlist, a dropped call rate (i.e., the ratio between failed calls and atotal number of calls requested), a transmitted radio power, trafficstatistical values associated with a cell, total average transmittedpower (e.g., in dBm), uplink total noise (e.g., in dBm), downlink/uplinkload factor (e.g., in percentage), uplink interference noise rise (e.g.,in dB), number of downlink/uplink radio links used, connection successrate (e.g., in percentage), average number of attempted users, averagenumber of connected users, average number of used codes, number ofhandovers of end-user equipments, ratio of handoff (e.g., inpercentage), connection success, downlink/uplink throughput (e.g., inkbps), etc. In some embodiments, the KPIs can include cell level KPIs(e.g., statistics associated with handover and neighbor list as listedabove), user equipment level KPIs (e.g., a signal strength indicator fora wireless communication device), as well as system level KPIs (e.g.,number of connected users across the network).

The network optimization device 101 can be any device configured tocontrol and/or coordinate one or more optimization processes (e.g.,hardware processes and/or software processes executed in hardware) forperforming optimization of network parameters in the wireless network100. In some embodiments, the network optimization device 101 can be,for example, a compute device, a server device, an application server, amobile device, a workstation, and/or the like. As shown in FIG. 1, thenetwork optimization device 101 can be directly or operatively coupledto the remaining devices within the wireless network 100. Specifically,the network optimization device 101 can be operatively coupled to thenetwork elements (e.g., the base stations 142, 144 and 146) via one ormultiple intermediate modules and/or devices such as, for example, acontroller (e.g., the controllers 112, 114 and 116) and/or the like. Insome embodiments, the network optimization device 101 can be coupled tothe remaining devices of the wireless network 100 via any suitableconnecting mechanism such as, for example, optical connections (e.g.,optical cables and optical connectors), electrical connections (e.g.,electrical cables and electrical connectors), wireless connections(e.g., wireless transceivers and antennas), and/or the like.Furthermore, while shown in FIG. 1 as a single device, in someembodiments, the functionality of the network optimization device 101can be distributed to multiple devices across the wireless network 100.

In some embodiments, the optimization process(es) for performingoptimization of network parameters in the wireless network 100 can beexecuted at a processor of the network optimization device 101. FIG. 2is a block diagram of a processor 220 of a network optimization device200, which is structurally and functionally similar to the networkoptimization device 101 in FIG. 1. Furthermore, the network optimizationdevice 200 can be included in a wireless network similar to the wirelessnetwork 100 in FIG. 1. Although not shown in FIG. 2, the networkoptimization device 200 can include other components such as a memory, aseries of communication ports, and/or the like. In some embodiments, thenetwork optimization device 200 can be a single physical device. Inother embodiments, the network optimization device 200 can includemultiple physical devices, each of which can include one or multiplemodules (shown in FIG. 2) and/or other components (not shown in FIG. 2)of the processor 220.

As shown in FIG. 2, the processor 220 includes a cluster partitioningmodule 222 and an optimization module 224. Although not shown in FIG. 2,in some embodiments, the processor 220 can include other modules and/orcomponents associated with performing the optimization process such as,for example, a monitor module (monitoring performance of networkelements controlled by the network optimization 200), a detector module(detecting any malfunction or other problem at network elementscontrolled by the network optimization 200), a communication module(transmitting control signals to and/or receiving data from networkelements controlled by the network optimization 200), and/or the like.Each module in the processor 220 can be any combination ofhardware-based module (e.g., a field-programmable gate array (FPGA), anapplication specific integrated circuit (ASIC), a digital signalprocessor (DSP)) and/or software-based module (e.g., a module ofcomputer code stored in memory and/or executed at the processor 220)capable of performing one or more specific functions associated withthat module. The processor 220 can be any suitable processor configuredto run and/or execute those modules.

The cluster partitioning module 222 can be configured to partition cellsof the wireless network into clusters based on performance indicators(e.g., KPIs) collected from network elements (e.g., base stationssimilar to the base stations 142, 144 and 146 in FIG. 1) of the wirelessnetworks. In some embodiments, the cluster partitioning module 222 canbe configured to receive the performance indicators from a networkdatabase operatively coupled to the network optimization device 200,which is similar to the network database 120 in FIG. 1. In some otherembodiments, the cluster partitioning module 222 can be configured toreceive the performance indicators from the network elements of thewireless network. The performance indicators received at the clusterpartitioning module 222 can include performance indicators associatedwith resolving optimization issues such as load balancing, coverageoptimization, capacity optimization, interference reduction, selfhealing, etc., and/or configuration parameters pertaining to theseperformance indicators.

In some embodiments, in addition to the performance indicators, thecluster partitioning module 222 can be configured to receivecharacteristic patterns associated with the received performanceindicators. Such characteristic patterns can be identified, based onperformance indicators previously collected from the network elements,by a pattern recognition process that is executed at a device (e.g., adevice hosting the network database) operatively coupled to the networkoptimization device 200. Specifically, the characteristic patterns canbe identified based on a relationship between network elementsexhibiting values of the performance indicators that are the same orsimilar as defined by the pattern recognition process. In some otherembodiments, such a pattern recognition process can be executed at thenetwork optimization device 200 based on the performance indicatorsreceived at the network optimization device 200.

Based on the received performance indicators and characteristicpatterns, the cluster partitioning module 222 can be configured topartition the network elements (or equivalently, the cells associatedwith the network elements) into clusters. The clustering can be based onany unit of time and/or space in which the performance indicators withthe same or similar values can be grouped together according to certainthresholds based on the characteristic patterns. Such thresholds can be,for example, a throughput in a unit of time, an average transmissionrate, a total number of dropped calls during a time period, etc. In someembodiments, for example, the clusters can be based on time of day(e.g., 3 PM to 4 PM), day of week (e.g., Tuesday), season in the year(e.g., summer), geographical areas, morphological areas, populationdensity contours, and/or the like.

In some embodiments, cluster partitioning can be based on a combinationof time and space considerations. That is, a cluster can include a setof network elements (or equivalently, a set of cells associated withthose network elements), each of which is associated with a specificgeographic location and a specific time period. For example, a firstcluster can include a first base station at a first geographic locationfor a first time period (e.g., 3 PM to 4 PM of a day), the first basestation at the first geographic location for a second time period (e.g.,6 PM to 7 PM of a day) different from the first time period, and asecond base station at a second geographic location (different from thefirst geographic location) for the first time period; a second clustercan include the first base station at the first geographic location forthe second time period, and the second network element at the secondgeographic location for the second time period. Thus, a network element(or equivalently, a cell associated with that network element) can beincluded in one cluster for a given time period and another cluster fora different time period.

In some embodiments, clusters can include mutually exclusive networkelements or overlapping network elements. In some embodiments, networkelements that do not specifically fit in and/or follow a specificcharacteristic pattern of a cluster can be included in that cluster. Forexample, a neighbor network element (e.g., an immediately-adjacentneighbor network element, a network element within a distance, etc.) ofa network element having the specific characteristic pattern can beincluded in the cluster. For another example, a neighbor network elementof a network element having a problem can be included in a commoncluster of network elements with that problem. Such a cluster can beused to monitor the performance of a region and/or to helpreduce/minimize any negative effects on a region that a SON process cancause by making changes to the cluster.

In some embodiments, clusters can include mutually exclusive timeperiods or overlapping time periods. In some embodiments, time periodsthat do not specifically fit in and/or follow the characteristic patternof a cluster can be included in that cluster. For example, a precedingtime period and/or a time period following (e.g., a time periodimmediately preceding or following the time period exhibiting acharacteristic pattern, a time period within a time range of the timeperiod exhibiting a characteristic pattern, etc.) a time period havingthe characteristic pattern can be included in the cluster. In such anexample, if the characteristic pattern is detected for a time period, acluster can be defined to include an amount of time before and/or afterthe time period along with the time period. This can help to avoid orminimize any negative effects a SON process my cause on other timeperiods near the time period exhibiting the characteristic pattern.

In some embodiments, cluster partitions can include a single boundary. Agroup of sub-clusters can be defined within a cluster. The clusters canbe dynamic with respect to space, network elements, and/or time,frequency or period of the characteristic patterns. Specifically, afterthe initial establishment of cluster partitions based on the receivedperformance indicators and characteristic patterns, the clusterpartitioning module 222 can be configured to continue refining thecluster partitions based on ongoing collection of performance indicatorsand/or characteristic patterns. For example, network elements can beadded to and/or removed from a cluster based on changing performanceindicators and/or characteristic patterns. Similarly, additional timeperiods, geographic areas and/or network elements can be added to and/orremoved from a cluster based on changing performance indicators and/orcharacteristic patterns. For another example, a cluster can be splitinto two or more smaller clusters or can be merged with another clusterbased on changing performance indicators and/or characteristic patterns.

After partitioning cells into clusters, the cluster partitioning module222 can be configured to define one or more recurring schedule sets forthe network elements of the wireless network based on the partitionedclusters. In some embodiments, the cluster partitioning module 222 canbe configured to define a recurring schedule set for each cluster. Sucha recurring schedule set defined for a cluster includes at least one setof time periods that recur periodically based on the characteristicpattern of that cluster. In some embodiments, as discussed above,network elements with the same or similar performance indicators in aset of time periods can be included in a cluster. As a result, therecurring schedule set defined for that cluster can include time periodsfrom that set of time periods, where each time period within therecurring schedule set is associated with the same or similarperformance indicator for a network element with the performanceindicators for the same or other network elements associated with theremaining time periods within the recurring schedule set. In otherwords, each time period included in the recurring schedule set definedfor the cluster is associated with a performance indicator within apredefined range of the performance indicators associated with theremaining time periods from the recurring schedule set.

For example, the cluster partitioning module 222 can define a firstrecurring schedule set for the first cluster illustrated above toinclude the first time period (e.g., 3 PM to 4 PM of a day) for thefirst base station, the first time period for the second base station,and the second time period (e.g., 6 PM to 7 PM of a day) for the firstbase station, where each time period (associated with a network element)within the first recurring schedule set is associated with a droppedcall rate within a predefined range (e.g., between 10% and 12%).Similarly, the cluster partitioning module 222 can define a secondrecurring schedule set for the second cluster illustrated above toinclude the second time period for the first base station and the secondtime period for the second base station, where each time period(associated with a network element) within the second recurring scheduleset is associated with throughput within a predefined range (e.g.,between 10 kbits/s and 12 kbits/s).

In some embodiments, the cluster partitioning module 222 can define arecurring schedule set with certain limitations or constraints. Forexample, a recurring schedule set can include one hour from 3 PM to 4 PMin each day in October only. For another example, a recurring scheduleset can include two hours from 7 AM to 8 AM and from 6 PM to 7 PM ineach day for the next 10 days only. In some embodiments, the clusterpartitioning module 222 can be configured to define more than onerecurring schedule set for a cluster, where one recurring schedule setis nested within another recurring schedule set. For example, a combinedrecurring schedule set for a cluster can include one hour from 3 PM to 4PM (as an inner recurring schedule set) in every Monday (as an outerrecurring schedule set). For another example, another combined recurringschedule set for a cluster can include each Tuesday and Friday (as aninner recurring schedule set) in each March and October every year (asan outer recurring schedule set). In some embodiments, the clusterpartitioning module 222 can be configured to define more than tworecurring schedule sets, where one recurring schedule set is nestedwithin another. An example of recurring schedule sets is illustrated anddescribed with respect to FIG. 3 below.

After defining the recurring schedule set(s), the cluster partitioningmodule 222 can be configured to send the defined recurring schedule setto the optimization module 224. The optimization module 224 can beconfigured to execute one or more optimization processes (e.g., SONprocesses) for the partitioned clusters. In some embodiments, theoptimization module 224 can perform a learning-based parameter settingprocess and a pattern-based SON operation process. In such embodiments,although not shown in FIG. 2, the optimization module 224 can includemore than one sub-module such as, for example, a learning-basedparameter setting sub-module and a pattern-based SON operationsub-module, each of which can be configured to perform the correspondingfunction as described below.

The optimization module 224 (or the learning-based parameter settingsub-module of the optimization module 224) can be configured to learnoptimal and/or improved parameter setting for network elements in thepartitioned clusters. In some embodiments, similar to the clusterpartitioning module 222, the optimization module 224 can receiveperformance indicators and/or characteristic patterns associated withthe network elements of a cluster. Such performance indicators andcharacteristic patterns can be collected or identified according to therecurring schedule set defined for that cluster. For example, only theperformance indicators during the time periods from the recurringschedule set are collected from the network elements in the cluster andused at the optimization module 224.

The optimization module 224 (or the learning-based parameter settingsub-module of the optimization module 224) can then be configured tolearn and/or determine optimal and/or improved parameter settings forthe partitioned clusters. In some instances, the optimization module 224can be configured to determine a set of optimal and/or improvedparameter setting for each partitioned cluster. If the clusterpartitions are based on a combination of time and space boundaries, theparameter settings for the corresponding cluster can change based onboth the location of the network elements and the time periodsassociated with the network elements that are included in the cluster.Subsequently, as described below with the pattern-based SON operationsub-module, the optimization module 224 can apply the optimal and/orimproved parameter settings to the corresponding network elements of theclusters. Additionally, after initially learning or determining theoptimal and/or improved parameter setting of network elements in acluster based on received performance indicators and characteristicpatterns, in some instances, the optimization module 224 can beconfigured to continuously refine the parameter setting based on ongoingcollection of performances and characteristic patterns.

In some embodiments, the time based clusters and time based clusterparameter settings can follow a nested routine. Specifically, parametersettings of time based clusters changing more frequently can be executedmore frequently and parameter settings of clusters changing lessfrequently can be superimposed on the current parameter settings. Forexample, a change in a parameter setting of a first network element in acluster as a result of the cluster entering a new characteristic patternbased on time and/or space can be applied to the first network element,while a parameter setting of a second network element in the samecluster not affected by the new characteristic pattern can remainunchanged. For example, the cluster partition of network elements duringthe days in spring may be different from the cluster partition ofnetwork elements during the days in fall. Additionally, the parametersettings for the network elements may change more rapidly during the daybased on time-of-day clusters. On top of these changes, some additionalchanges in the parameter settings may occur as the season changes fromspring to summer, etc. In summary, each cluster can behave dynamicallywith respect to the network elements within that cluster, the timeperiods at which that cluster is active, and the period of repetitionassociated with that cluster.

After the cluster partitioning and cluster parameter setting phases arecompleted, the optimization module 224 (or the pattern-based SONoperation sub-module of the optimization module 224) can be configuredto execute one or more pattern-based SON operations for the clustersbased on the parameter settings determined for those clusters, as wellas performance indicators that are newly collected from those clusters.In some embodiments, the pattern-based SON operations can be executed ona per-cluster basis. For example, the learning-based parameter settingdetermined for a cluster and a set of newly collected performanceindicators for that cluster can be loaded into the pattern-based SONoperation sub-module, where a SON process can be executed specificallyfor that cluster. As a result, the SON process can generate one or moreparameter changes (e.g., metric value(s)) for the network elements ofthat cluster according to the recurring schedule set defined for thatcluster. Subsequently, the optimization module 224 can be configured togenerate and send one or more instruction signals including theparameter change(s) (e.g., metric value(s)) to the network elements ofthe cluster, such that the parameter change(s) can be implemented at thecorresponding network elements according to the recurring schedule setdefined for the cluster.

In some embodiments, for example, the SON processes executed at theoptimization module 224 can be similar to the SON processes shown anddescribed in U.S. Patent Application Publication No. 2011/0090820, filedOct. 16, 2009 and entitled “Self-Optimizing Wireless Network,” which isincorporated herein by reference in its entirety. Such SON processes canbe an antenna-based SON process, a parameter-based SON process, or acombined antenna- and parameter-based SON process. Such SON processescan include, for example, a load balancing SON process, a co-channelinterference SON process, a neighbor list SON process, a handoveroptimization SON process, a self-healing SON process, and/or the like.In some embodiments, the parameters to be changed can be a metricassociated with an antenna of a network element such as a tilt of theantenna, or a parameter of other type such as a parameter used in analgorithm executed at a network element.

For example, a cluster can be identified by the cluster partitioningmodule 222 to include a set of 10 specific network elements from 6 AM to10 AM. In other words, a recurring schedule set including a time periodfrom 6 AM to 10 AM for those 10 network elements can be defined for thecluster at the cluster partitioning module 222. Then, at 6 AM, a currentset of parameters for the cluster can be loaded into or implemented inthe 10 network elements of the cluster. A set of performance indicatorscan be monitored and collected from the network elements in the clusteraccording to the recurring schedule (i.e., 6 AM to 10 AM). The collectedperformance indicators can then be sent to the optimization module 224,where a SON process can be executed based on the collected performanceindicators and a parameter setting determined for that cluster at thelearning-based parameter setting sub-module of the optimization module224. As a result, if the SON process determines any changes for anyparameter for the cluster, the optimization module 224 can send aninstruction signal including the parameter changes to the networkelements of the cluster. These changes can then be applied (loaded orimplemented) at these 10 network elements according to the recurringschedule (e.g., at 6 AM in next day). As such, a round of optimizationis completed for network elements of that cluster.

For another example, the SON process may also determine changes at thebeginning of each hour within the time period of the recurring scheduleset (e.g., at 6, 7, 8, 9, and 10 AM) for the cluster. Furthermore, at 11AM, a parameter of a network element within the cluster can be changeddependent on another cluster's values (because 11 AM is outside therecurring schedule for the previous cluster). The next day, however,changes made at 6 AM for the cluster can be based primarily on thecalculations performed, changes made, and/or observations made at 10 AMof the previous day on the cluster.

Additionally, in some embodiments, during the pattern-based SONoperations, if the characteristic patterns for clusters change as aresult of the operations (e.g., ongoing pattern recognition and/orclassification) executed at the cluster partitioning module and/or thelearning-based parameter setting module as described above, thecorresponding cluster(s) can be dynamically and repeatedly redefined toreflect the modified characteristic patterns.

While shown in FIG. 2 as being included in the processor 220 of thenetwork optimization device 200, in other embodiments, a clusterpartitioning module or an optimization module can communicate with anyother device of the wireless network via an application programminginterface (API) of a network module and/or application, a networkprocess, an intermediary device, and/or any other suitable means.Additionally, the cluster partitioning module can receive networkinformation and/or data (e.g., performance indicators, characteristicpatterns) from the network database or any other device in any suitableformat such as, for example, text files, a file format associated with anetwork, and/or the like. Similarly, the optimization module can sendinstructions of parameter changes to network elements in any suitablemeans.

FIG. 3 is a schematic illustration of a timeline for operations of anetwork optimization device, which is structurally and functionallysimilar to the network optimization device 101 and 200 shown anddescribed with respect to FIGS. 1 and 2. The network optimization deviceis within a wireless network similar to the wireless network 100 in FIG.1, and operatively coupled to a set of network elements of the wirelessnetwork. Similar to the network optimization device 200 in FIG. 2, thenetwork optimization device includes at least a cluster partitioningmodule similar to the cluster partitioning module 222 in FIG. 2 and anoptimization module similar to the optimization module 224 in FIG. 2.

At the first time 352, the cluster partitioning module of the networkoptimization device can be configured to receive a first set ofperformance indicators that are collected from the network elementsduring time periods from a first instance of a set of time periods. Asshown in FIG. 3, the first instance of the set of time periods includesat least time period 312 and time period 314. For example, the firstinstance of the set of time periods can include each hour (e.g., 12 AMto 1 AM, 1 AM to 2 AM, etc.) of a first day. The cluster partitioningmodule can be configured to receive a set of dropped call rates (e.g.,10%, 15%) for all the network elements within the wireless network,which are measured for each hour of the first day.

After receiving the first set of performance indicators, the clusterpartitioning module can be configured to define a cluster based on thefirst set of performance indicators. Each network element at each timeperiod from the cluster has a performance indicator that is from thefirst set of performance indicators and within a predefined range. Asdiscussed above, the cluster can be a set of network elements based onboth space and time. Thus, within the cluster, a network element can beassociated with different time periods, and more than one networkelement can be associated with the same time period. For example, thepredefined range can be between a dropped call rate of 10% and a droppedcall rate of 12%. The cluster partitioning module can define a clusterto include a first network element during 2 AM to 3 AM that has adropped call rate of 10%, the first network element during 5 PM to 6 PMthat has a dropped call rate of 11%, a second network element during 2AM to 3 AM that has a dropped call rate of 10.5%, etc.

Furthermore, the cluster partitioning module can be configured to definea recurring schedule set for the cluster. Such a recurring schedule setincludes recurring time periods associated with network elements, whichare included in the cluster. For example, the recurring schedule set caninclude 2 AM to 3 AM and 5 PM to 6 PM for the first network element, and2 AM to 3 AM for the second network element, on each day after the firstday.

Performance indicators of the network elements can then be monitored andcollected according to the recurring schedule set. The optimizationmodule of the network optimization device can be configured to receivethe collected performance indicators. As show in FIG. 3, the time period322 (associated with a first network element) and the time period 324(associated with a second network element, which can be the same ordifferent than the first network element) are included in the recurringschedule set, and also included in a second instance of the set of timeperiods. For example, if the recurring schedule set includes 2 AM to 3AM and 5 PM to 6 PM for the first network element, and 2 AM to 3 AM forthe second network element, then performance indicators such as thedropped call rate can be collected from the first network elements for 2AM to 3 AM and 5 PM to 6 PM on a second day after the first day, andcollected from the second network element for 2 AM to 3 AM on the secondday. As shown in FIG. 3, for example, the time period 322 can represent2 AM to 3 AM on the second day for the first network element, and thetime period 324 can represent 5 PM to 6 PM on the second day for thefirst network element.

At the second time 354 after the first time 352, the optimization modulecan be configured to receive a second set of performance indicatorscollected according to the recurring schedule as described above. Asdiscussed with respect to FIG. 2, the optimization module can then beconfigured to determine an optimal or improved parameter setting for thecluster, and then execute a SON process to define one or more metricvalues for the cluster based on the determined parameter setting and thesecond set of performance indicators. The metric value(s) are definedfor parameters of the network elements of the cluster to optimize orfurther improve the performance of those network elements. Theoptimization module can be configured to send an instruction signalincluding the metric values to the corresponding network elements, suchthat the metric values can be implemented at those network elements. Insome embodiments, the metric values can be implemented at thecorresponding network elements according to the recurring schedule(i.e., during each time period in the future from the recurring scheduleset).

For example, the optimization module can receive a set of dropped callrates including a dropped call rate of 11% for the first network elementduring 2 AM to 3 AM on the second day, a dropped call rate of 11.5% forthe first network element during 5 PM to 6 PM on the second day, and adropped call rate of 12% for the second network element during 2 AM to 3AM on the second day. Based on the data, the optimization module definesa new tilt value for the antennas of the network elements in thecluster. The optimization module sends a set of signals to the networkelements, instructing the network elements to change their antenna tiltsaccordingly during each time period from the recurring schedule set inthe future (i.e., starting from a third day after the second day). Thus,the first network element changes its antenna tilt to the new tilt valueduring the time period 332 (e.g., 2 AM to 3 AM on the third day) and thetime period 334 (e.g., 5 PM to 6 PM on the third day).

In some embodiments, the network optimization device can be configuredto define a cluster (or equivalently, a recurring schedule set) andmetric values for optimization of that cluster based on a single set ofperformance indicators. For example, after receiving the first set ofdropped call rates from the first day at the first time 352, the networkoptimization device can be configured to define a recurring schedule set(including the time periods 322, 324, 332 and 334) and define a new tiltvalue for the antennas of the network elements included in the clusterbased on the first set of dropped call rates. As a result, the new tiltvalue can be sent to and implemented at each corresponding networkelement during each time period in the future according to the recurringschedule (e.g., the time periods 322 and 324 in the second day, the timeperiods 332 and 334 in the third day, etc.).

In some embodiments, the network optimization device can be configuredto continuously monitor the performance of the cluster. Based on theresults from the monitoring, the network optimization device can beconfigured to repeat the process described above to dynamically optimizeor improve performance of the cluster, and/or to modify the recurringschedule set. For example, the network optimization device can beconfigured to receive a third set of performance indicators (e.g.,dropped call rates) from the network elements that are collected duringa third instance of the set of time periods (e.g., in the third day)according to the recurring schedule. Similar to the process describedabove, the network optimization device can be configured to modifyparameters of the network elements and/or modify the recurring schedulebased on the third set of performance indicators.

In some embodiments, as described above with respect to FIG. 2, anetwork optimization device can define more than one recurring schedulesets, such that a recurring schedule set with relatively short timeperiods can be nested within another recurring schedule set withrelatively long time periods. For example, as shown in FIG. 3, therecurring schedule sets 382, 384 and 386 include time periods that aremeasured using quarter of an hour as a unit (e.g., 2:00 AM to 2:15 AM,5:30 PM to 5:45 PM, etc.). Such recurring schedule sets can be nestedwithin the recurring schedule set described above, which includes timeperiods that are measured using hour as a unit (e.g., 2 AM to 3 AM, 5 PMto 6 PM, etc.).

Additionally, the recurring schedule sets nested within the previousrecurring schedule set can be different from one time period of theprevious recurring schedule set to another time period of the previousrecurring schedule set. For example, the recurring schedule set 382included in the time period 312 (e.g., 2 AM to 3 AM in the first day) ofthe previous recurring schedule set can include 2:00 AM to 2:15 AM and2:30 AM to 2:45 AM; the recurring schedule set 384 included in the timeperiod 322 (e.g., 2 AM to 3 AM in the second day) of the previousrecurring schedule set can include 2:00 AM to 2:15 AM and 2:45 AM to3:00 AM; and the recurring schedule set 386 included in the time period332 (e.g., 2 AM to 3 AM in the third day) of the previous recurringschedule set can include 2:15 AM to 2:30 AM and 2:30 AM to 2:45 AM.

FIG. 4 is a flow chart illustrating a method 400 for defining recurringschedule sets and using the recurring schedule sets for optimization,according to an embodiment. The code representing instructions toperform method 400 can be stored in, for example, a non-transitoryprocessor-readable medium (e.g., a memory) in a network optimizationdevice that is similar to the network optimization device 101 and 200shown and described with respect to FIGS. 1 and 2. The code can beexecuted by a processor of the network optimization device that issimilar to the processor 220 in FIG. 2. The code stored in thenon-transitory processor-readable medium includes code to be executed bythe processor to cause the network optimization device to operate thefunctions illustrated in FIG. 4 and described as follows.

At 402, a cluster partitioning module of the network optimization devicecan receive, at a first time, a first set of performance indicators ofat least one cell (or equivalently, at least one network element) for afirst instance of a set of time periods. The first set of performanceindicators can be received from a network database (similar to thenetwork database 120 in FIG. 1) or from network elements of a wirelessnetwork that includes the network optimization device. The performanceindicators can include, for example, KPIs collected from the networkelements during the first instance of the set of time periods.

At 404, the cluster partitioning module of the network optimizationdevice can define a first recurring schedule set including at least twotime periods from the set of time periods. Each time period from therecurring schedule set is associated with at least one performanceindicator from the first set of performance indicators. Furthermore,each performance indicator associated with a time period from the firstrecurring schedule set is within a predefined range of each otherperformance indicator associated with the remaining time periods fromthe first recurring schedule set. Thus, cells (associated with thecorresponding time periods) having the same or similar performance areassociated with the common recurring schedule set (or equivalently, acommon cluster).

At 406, the cluster partitioning module can send an indication of thefirst recurring schedule set to a SON process module at a second timeafter the first time, such that the SON process module performs a SONprocess for the at least one cell during each time period from the firstrecurring schedule set and from a second instance of the set of timeperiods. The SON process module can be similar to the optimizationmodule 224 shown and described with respect to FIG. 2. As a result ofperforming the SON process, the SON process module can define one ormore parameter changes, and send the parameter changes (e.g., viainstruction signals) to the cells such that the parameter changes can beimplemented at the cells according to the first recurring scheduleduring the second instance of the set of time periods.

At 408, the network optimization device can receive, at a third timeafter the second time, a second set of performance indicators of the atleast one cell for the second instance of the set of time periods. Basedon the second set of performance indicators, at 410, clusterpartitioning module of the network optimization device can modify thefirst recurring schedule set to define a second recurring schedule set.For example, the second recurring schedule set can be defined to beassociated with cells (associated with corresponding time periods)having the same or similar performance according to the second set ofperformance indicators. As the second set of performance indicators maybe different from the first set of performance indicators due to theparameter changes during the second instance of the set of time periods,the second recurring schedule set may be different from the firstrecurring schedule set. Furthermore, similar to the step 406, thecluster partitioning module can send an indication of the secondrecurring schedule set to the SON process module at a fourth time afterthe third time, such that the SON process module performs the SONprocess for the at least one cell during each time period from thesecond recurring schedule set and from a third instance of the set oftime periods.

Some embodiments described herein relate to a computer storage productwith a non-transitory computer-readable medium (also can be referred toas a non-transitory processor-readable medium) having instructions orcomputer code thereon for performing various computer-implementedoperations. The computer-readable medium (or processor-readable medium)is non-transitory in the sense that it does not include transitorypropagating signals per se (e.g., a propagating electromagnetic wavecarrying information on a transmission medium such as space or a cable).The media and computer code (also can be referred to as code) may bethose designed and constructed for the specific purpose or purposes.Examples of non-transitory computer-readable media include, but are notlimited to: magnetic storage media such as hard disks, floppy disks, andmagnetic tape; optical storage media such as Compact Disc/Digital VideoDiscs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), andholographic devices; magneto-optical storage media such as opticaldisks; carrier wave signal processing modules; and hardware devices thatare specially configured to store and execute program code, such asApplication-Specific Integrated Circuits (ASICs), Programmable LogicDevices (PLDs), Read-Only Memory (ROM) and Random-Access Memory (RAM)devices. Other embodiments described herein relate to a computer programproduct, which can include, for example, the instructions and/orcomputer code discussed herein.

Examples of computer code include, but are not limited to, micro-code ormicro-instructions, machine instructions, such as produced by acompiler, code used to produce a web service, and files containinghigher-level instructions that are executed by a computer using aninterpreter. For example, embodiments may be implemented using Java,C++, or other programming languages (e.g., object-oriented programminglanguages) and development tools. Additional examples of computer codeinclude, but are not limited to, control signals, encrypted code, andcompressed code.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, notlimitation, and various changes in form and details may be made. Wheremethods described above indicate certain events occurring in certainorder, the ordering of certain events may be modified. Additionally,certain of the events may be performed concurrently in a parallelprocess when possible, as well as performed sequentially as describedabove. Any portion of the apparatus and/or methods described herein maybe combined in any combination, except mutually exclusive combinations.The embodiments described herein can include various combinations and/orsub-combinations of the functions, components and/or features of thedifferent embodiments described.

What is claimed is:
 1. An apparatus, comprising: a cluster partitioningmodule implemented in at least one of a memory or a processing device,the cluster partitioning module configured to receive, at a first time,a first set of performance indicators of at least one cell of a networkfor a first instance of a plurality of time periods, the clusterpartitioning module configured to define a recurring schedule setincluding at least two time periods from the plurality of time periods,each time period from the recurring schedule set is associated with atleast one performance indicator (1) from the first set of performanceindicators and (2) within a predefined range of at least one performanceindicator associated with the remaining time periods from the recurringschedule set and from the first set of performance indicators; and anoptimization module configured to receive, at a second time after thefirst time, a second set of performance indicators of the at least onecell for a second instance of the plurality of time periods, theoptimization module configured to define a metric value associated withthe recurring schedule set based on each performance indicator (1)associated with each time period from the recurring schedule set and (2)from the second set of performance indicators, the optimization moduleconfigured to send the metric value to at least one network elementassociated with the at least one cell such that the at least one networkelement changes, at each time period (1) from a third instance of theplurality of time periods and (2) from the recurring schedule set, animplementation of the network based on the metric value when the metricvalue is associated with a network configuration change.
 2. Theapparatus of claim 1, wherein the metric value is a first metric value,the optimization module is configured to define, after the first timebut before the second time, a second metric value for the recurringschedule set based on the first set of performance indicators, theoptimization module configured to send the second metric value to the atleast one network element such that the at least one network elementchanges, at each time period (1) from the second instance of theplurality of time periods and (2) from the recurring schedule set, animplementation of the network based on the second metric value when thesecond metric value is associated with a network configuration change.3. The apparatus of claim 1, wherein the cluster partitioning module isconfigured to receive, at a third time after the second time, a thirdset of performance indicators of the at least one cell for the thirdinstance of the plurality of time periods, the cluster partitioningmodule configured to modify the recurring schedule set based on thethird set of performance indicators.
 4. The apparatus of claim 1,wherein the recurring schedule set is a first recurring schedule set,the cluster partitioning module is configured to define a secondrecurring schedule set to include a time period from the plurality oftime periods and immediately preceding at least one of the at least twotime periods, the time period is associated with at least oneperformance indicator (1) from the first set of performance indicatorsand (2) not within the predefined range of the at least one performanceindicator associated with the first recurring schedule, the optimizationmodule configured to define the metric value associated with the secondrecurring schedule set, the optimization module configured to send themetric value to the at least one network element associated with the atleast one cell such that the at least one network element changes, ateach time period (1) from the third instance of the plurality of timeperiods and (2) from the second recurring schedule set, animplementation of the network based on the metric value when the metricvalue is associated with a network configuration change.
 5. Theapparatus of claim 1, wherein the optimization module is at least one ofan antenna-based Self Optimizing Network (SON) process module or aparameter-based SON process module.
 6. The apparatus of claim 1, whereinthe metric value is associated with a tilt of an antenna of the at leastone network element.
 7. The apparatus of claim 1, wherein theoptimization module is at least one of a load balancing Self OptimizingNetwork (SON) process module, a co-channel interference SON processmodule, a neighbor list SON process module, a handover optimization SONprocess module or a self-healing SON process module.
 8. The apparatus ofclaim 1, wherein the at least one cell includes a plurality of cells. 9.The apparatus of claim 1, wherein the at least one cell includes aplurality of cells, a subset of time periods from the recurring scheduleset and associated with a first cell from the plurality of cells beingdifferent from a subset of time periods from the recurring schedule setand associated with a second cell from the plurality of cells.
 10. Theapparatus of claim 1, wherein the recurring schedule set is a firstrecurring schedule set, the plurality of time periods is a firstplurality of time periods, the optimization module configured to definea second recurring schedule set including at least two time periods froma second plurality of time periods, each instance of the secondplurality of time periods including at least one instance of the firstplurality of time periods, a duration of each instance of the firstplurality of time periods being less than a duration of each instance ofthe second plurality of time periods.
 11. A non-transitoryprocessor-readable medium storing code representing instructions to beexecuted by a processor, the code comprising code to cause the processorto: receive, at a first time, a first set of performance indicators ofat least one cell for a first instance of a plurality of time periods;define a first recurring schedule set including at least two timeperiods from the plurality of time periods, each time period from therecurring schedule set is associated with at least one performanceindicator (1) from the first set of performance indicators and (2)within a predefined range of at least one performance indicator from thefirst set of performance indicators and associated with the remainingtime periods from the first recurring schedule set; send, to a SelfOptimizing Network (SON) process module at a second time after the firsttime, an indication of the first recurring schedule set such that theSON process module performs a SON process for the at least one cellduring each time period (1) from the first recurring schedule set and(2) from a second instance of the plurality of time periods; receive, ata third time after the second time, a second set of performanceindicators of the at least one cell for the second instance of theplurality of time periods; and modify the first recurring schedule setbased on the second set of performance indicators to define a secondrecurring schedule set.
 12. The non-transitory processor-readable mediumof claim 11, wherein the SON process module is at least one of a loadbalancing SON process module, a co-channel interference SON processmodule, a neighbor list SON process module, a handover optimization SONprocess module or a self-healing SON process module.
 13. Thenon-transitory processor-readable medium of claim 11, the code furthercomprising code to cause the processor to: send, to the SON processmodule at a fourth time after the third time, an indication of thesecond recurring schedule set such that the SON process module performsthe SON process within the at least one cell during each time period (1)from the second recurring schedule set and (2) from a third instance ofthe plurality of time periods.
 14. The non-transitory processor-readablemedium of claim 11, wherein the second recurring schedule set includes atime period not included in the first recurring schedule set and fromthe plurality of time periods.
 15. An apparatus, comprising: a clusterpartitioning module implemented in at least one of a memory or aprocessing device, the cluster partitioning module configured to receivea set of performance indicators of a plurality of cells of a network fora first instance of a plurality of time periods, the clusterpartitioning module configured to define a recurring schedule setincluding at least (1) a first time period from the plurality of timeperiods and associated with a first cell from the plurality of cells and(2) a second time period from the plurality of time periods andassociated with a second cell from the plurality of cells, the firstcell at the first time period being associated with at least oneperformance indicator (1) from the set of performance indicators and (2)within a predefined range of at least one performance indicatorassociated with the second cell at the second time period and from theset of performance indicators; and an optimization module configured todefine at least one metric value for the recurring schedule set based atleast in part on the at least one performance indicator associated withthe first cell at the first time period and the at least one performanceindicator associated with the second cell at the second time period, theoptimization module configured to send the at least one metric value toa network element associated with the first cell such that the networkelement associated with the first cell changes, at the first time periodfrom a second instance of the plurality of time periods, animplementation of the network based on the metric value when the metricvalue is associated with a network configuration change at the firsttime period from the second instance of the plurality of time periods,the optimization module configured to send the at least one metric valueto a network element associated with the second cell such that thenetwork element associated with the second cell changes, at the secondtime period from the second instance of the plurality of time periods,an implementation of the network based on the metric value when themetric value is associated with a network configuration change at thesecond time period from the second instance of the plurality of timeperiods.
 16. The apparatus of claim 15, wherein the first time period isdifferent from the second time period.
 17. The apparatus of claim 15,wherein the set of performance indicators is a first set of performanceindicators, the cluster partitioning module configured to receive thefirst set of performance indicators at a first time, the clusterpartitioning module is configured to receive, at a second time after thefirst time, a second set of performance indicators of the plurality ofcells for the second instance of the plurality of time periods, thecluster partitioning module configured to modify the recurring scheduleset based on the second set of performance indicators.
 18. The apparatusof claim 15, wherein the optimization module is at least one of a loadbalancing Self Optimizing Network (SON) process module, a co-channelinterference SON process module, a neighbor list SON process module, ahandover optimization SON process module or a self-healing SON processmodule.
 19. The apparatus of claim 15, wherein the at least one metricvalue is associated with a tilt of an antenna of the at least onenetwork element.
 20. The apparatus of claim 15, wherein the optimizationmodule is at least one of an antenna-based Self Optimizing Network (SON)process module or a parameter-based SON process module.